Multiple measures-based chaotic time series for traffic flow prediction based on Bayesian theory

Li, Yongfu; Xiao, Jiang; Zhu, Hao; He, Xiaozheng; Peeta, Srinivas; Zheng, Taixiong; Li, Yinguo

doi:10.1007/s11071-016-2677-5

Cited by 62 publications

(19 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Subsequently, it was analysed [10] whether there are chaos characteristics in the urban traffic flow time series. Further, a Bayesian theory-based multiple measures chaotic traffic flow time series prediction algorithm is proposed and the numerical experiments verified its effectiveness [11]. Later, some scholars combined chaos theory with other methods to predict shortterm traffic, and got better results [12].…”

Section: Introductionmentioning

confidence: 90%

“…When the data center no longer changes, the expansion constant closely related to it also stops changing. Then the connection weight from hidden layer to the output layer can be directly calculated by the least squares method, namely: (11) In this section, we use original RBF neural network to make short-term traffic prediction of Lan-Hai expressway. For the purpose of comparison, the normalization of the data and the design of input layer and output layer using the same parameters as in wavelet neural network, namely, the number of input layer neurons is 8, the number of output layer neurons is 1.…”

Section: Original Rbf Neural Network Prediction Modelmentioning

confidence: 99%

See 1 more Smart Citation

Short-Term Traffic Prediction Based on Genetic Algorithm Improved Neural Network

Zeng

Zhang

et al. 2020

Teh. vjesn.

View full text Add to dashboard Cite

This paper takes the time series of short-term traffic flow as research object. The delay time and embedding dimension are calculated by CC algorithm, and the chaotic characteristics of the time series are verified by small data sets method.Then based on the neural network prediction model and the chaotic phase space reconstruction theory, the network topology is determined, and the prediction is conducted by the wavelet neural network and RBF neural network using Lan-Hai expressway experimental data. The results show that the prediction effect of RBF neural network is better. Due to the poor stability of the network caused by the initial parameters randomness, the genetic algorithm is used to optimize the initial parameters. The results show that the prediction error of the optimized wavelet neural network or RBF neural network is reduced by more than 10%, and prediction accuracy of the latter is better.

show abstract

Section: Introductionmentioning

confidence: 90%

Section: Original Rbf Neural Network Prediction Modelmentioning

confidence: 99%

Short-Term Traffic Prediction Based on Genetic Algorithm Improved Neural Network

Zeng

Zhang

et al. 2020

Teh. vjesn.

View full text Add to dashboard Cite

show abstract

“…At present, network traffic analysis and modeling is a key issue in network traffic measurement. The network traffic time series should be modeled as a strongly nonlinear system . Therefore, kernel‐based ν‐SVR is adopted to analyze and model network traffic flows in detail.…”

Section: Network Traffic Prediction Based On Svrmentioning

confidence: 99%

An adaptive network traffic prediction approach for LDoS attacks detection

Zhang

et al. 2017

Int J Communication

View full text Add to dashboard Cite

Low-rate denial of service (LDoS) attacks reduce throughput and degrade quality of service (QoS) of network services by sending out attack packets with relatively low average rate. LDoS attack flows are difficult to detect from normal traffic since it has the property of low average rate. The research on network traffic analysis and modeling shows that network traffic measurement data are irregular nonlinear time series. To characterize and analyze network traffic between attack and non-attack situations, the adaptive normal and abnormal νsupport vector regression (ν-SVR) prediction models are constructed on the basis of the reconstructed phase space. In this paper, the dimension of reconstructed phase space for ν-SVR is optimized by Bayesian information criteria method, and the parameter in the radial basis function is adaptively adjusted by minimizing the within-class distance and maximizing the between-class distance in the feature space. The nonthreshold decision function is obtained through calculating the prediction error of adaptive normal and abnormal ν-SVR prediction models, which is adopted to detect LDoS attacks. Experiments in NS-2 environment show that the adaptive ν-SVR prediction model can effectively predict the network traffic measurement time series, and the probability distribution of time series generated by the adaptive ν-SVR prediction model is quite similar to that of the network traffic measurement data. Experiments also clearly demonstrate the superiority of the proposed approach in LDoS attacks detection.KEYWORDS kernel methods, low-rate denial of service (LDoS), network traffic, nonlinear time series analysis, support vector regression (SVR) of LDoS attacks can be denoted as R × L/T. During LDoS attacking, high intensity pulses are periodically sent to a victim in a specific short interval. The attack duration is short while the time of silence in each period is long, so LDoS attacks attempt to send attack traffic flows at low average rate to elude detection by counter-DoS mechanisms. At present, LDoS detection methods can be divided into frequency domain-based methods and time domain-based methods. 7 In the frequency domain-based methods, He et al 8 proposed a detection system-based wavelet analysis against LDoS attacks, in which the wavelet multiscale analysis was used to extract 5 feature indices of LDoS flows, and back propagation (BP) neural network was used to detect LDoS attacks. Simulation results showed that the detection system-based wavelet analysis can achieve high detection rate (DR) with low computation cost. Wu et al 9 established a spectral energy distribution probability model for detecting LDoS attacks. A probabilistic model was constructed on the basis of LDoS attacks and normal transmission control protocol traffic, and the detection criterion for LDoS attack was calculated by Fourier transform. This approach achieved favorable detection effectiveness, and the DR was obtained by hypothesis test. Zhang et al 10 proposed an adaptive kernel principal component analysi...

show abstract

“…Based on the trajectory data, the prediction of traffic flow and research of multiple hot spots attracted a lot of attention. Methods based on time series have been widely used for traditional hot spot analysis in the field of intelligent transportation [1]- [3]. These methods are often appropriate for single targets, and not for multiple models that needed to be trained within complex scenarios.…”

Section: Introductionmentioning

confidence: 99%

A Flood-Discharge-Based Spatio-Temporal Diffusion Method for Multi-Target Traffic Hotness Construction From Trajectory Data

Zhang

Qin

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Multiple measures-based chaotic time series for traffic flow prediction based on Bayesian theory

Cited by 62 publications

References 35 publications

Short-Term Traffic Prediction Based on Genetic Algorithm Improved Neural Network

Short-Term Traffic Prediction Based on Genetic Algorithm Improved Neural Network

An adaptive network traffic prediction approach for LDoS attacks detection

A Flood-Discharge-Based Spatio-Temporal Diffusion Method for Multi-Target Traffic Hotness Construction From Trajectory Data

Contact Info

Product

Resources

About